Overview

Dataset statistics

Number of variables16
Number of observations119711
Missing cells1926
Missing cells (%)0.1%
Duplicate rows199
Duplicate rows (%)0.2%
Total size in memory15.5 MiB
Average record size in memory136.0 B

Variable types

Categorical3
Text1
Numeric11
DateTime1

Alerts

Dataset has 199 (0.2%) duplicate rowsDuplicates
so2 is highly overall correlated with SOiHigh correlation
no2 is highly overall correlated with NoiHigh correlation
rspm is highly overall correlated with spm and 2 other fieldsHigh correlation
spm is highly overall correlated with rspm and 5 other fieldsHigh correlation
pm2_5 is highly overall correlated with spm and 3 other fieldsHigh correlation
SOi is highly overall correlated with so2High correlation
Noi is highly overall correlated with no2High correlation
SPMi is highly overall correlated with rspm and 5 other fieldsHigh correlation
PMi is highly overall correlated with spm and 3 other fieldsHigh correlation
AQI is highly overall correlated with rspm and 5 other fieldsHigh correlation
AQI_Range is highly overall correlated with spm and 2 other fieldsHigh correlation
type has 1925 (1.6%) missing valuesMissing
RSPMi has 19323 (16.1%) zerosZeros

Reproduction

Analysis started2023-11-18 06:46:54.074117
Analysis finished2023-11-18 06:47:23.858344
Duration29.78 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

state
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
West Bengal
22463 
Gujarat
21279 
Tamil Nadu
20597 
Madhya Pradesh
19920 
Odisha
19279 
Other values (4)
16173 

Length

Max length20
Median length11
Mean length9.0152534
Min length3

Characters and Unicode

Total characters1079225
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDadra & Nagar Haveli
2nd rowDadra & Nagar Haveli
3rd rowDadra & Nagar Haveli
4th rowDadra & Nagar Haveli
5th rowDadra & Nagar Haveli

Common Values

ValueCountFrequency (%)
West Bengal 22463
18.8%
Gujarat 21279
17.8%
Tamil Nadu 20597
17.2%
Madhya Pradesh 19920
16.6%
Odisha 19279
16.1%
Delhi 8551
 
7.1%
Goa 6206
 
5.2%
Daman & Diu 782
 
0.7%
Dadra & Nagar Haveli 634
 
0.5%

Length

2023-11-18T12:17:24.020805image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T12:17:24.244804image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
west 22463
12.1%
bengal 22463
12.1%
gujarat 21279
11.4%
tamil 20597
11.1%
nadu 20597
11.1%
madhya 19920
10.7%
pradesh 19920
10.7%
odisha 19279
10.4%
delhi 8551
 
4.6%
goa 6206
 
3.3%
Other values (6) 4882
 
2.6%

Most occurring characters

ValueCountFrequency (%)
a 196194
18.2%
d 80350
 
7.4%
e 74031
 
6.9%
h 67670
 
6.3%
66446
 
6.2%
s 61662
 
5.7%
l 52245
 
4.8%
i 49843
 
4.6%
t 43742
 
4.1%
u 42658
 
4.0%
Other values (19) 344384
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 826622
76.6%
Uppercase Letter 184741
 
17.1%
Space Separator 66446
 
6.2%
Other Punctuation 1416
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 196194
23.7%
d 80350
9.7%
e 74031
 
9.0%
h 67670
 
8.2%
s 61662
 
7.5%
l 52245
 
6.3%
i 49843
 
6.0%
t 43742
 
5.3%
u 42658
 
5.2%
r 42467
 
5.1%
Other values (7) 115760
14.0%
Uppercase Letter
ValueCountFrequency (%)
G 27485
14.9%
B 22463
12.2%
W 22463
12.2%
N 21231
11.5%
T 20597
11.1%
M 19920
10.8%
P 19920
10.8%
O 19279
10.4%
D 10749
 
5.8%
H 634
 
0.3%
Space Separator
ValueCountFrequency (%)
66446
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1416
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1011363
93.7%
Common 67862
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 196194
19.4%
d 80350
 
7.9%
e 74031
 
7.3%
h 67670
 
6.7%
s 61662
 
6.1%
l 52245
 
5.2%
i 49843
 
4.9%
t 43742
 
4.3%
u 42658
 
4.2%
r 42467
 
4.2%
Other values (17) 300501
29.7%
Common
ValueCountFrequency (%)
66446
97.9%
& 1416
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1079225
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 196194
18.2%
d 80350
 
7.4%
e 74031
 
6.9%
h 67670
 
6.3%
66446
 
6.2%
s 61662
 
5.7%
l 52245
 
4.8%
i 49843
 
4.6%
t 43742
 
4.1%
u 42658
 
4.0%
Other values (19) 344384
31.9%
Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:24.672949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length17
Median length13
Mean length7.1725572
Min length4

Characters and Unicode

Total characters858634
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDaman
2nd rowDaman
3rd rowDaman
4th rowDaman
5th rowDaman
ValueCountFrequency (%)
delhi 8551
 
6.9%
kolkata 7733
 
6.3%
chennai 6646
 
5.4%
ahmedabad 6256
 
5.1%
howrah 3601
 
2.9%
indore 3456
 
2.8%
bhopal 3449
 
2.8%
surat 3441
 
2.8%
coimbatore 3268
 
2.7%
nagda 3038
 
2.5%
Other values (91) 73699
59.9%
2023-11-18T12:17:25.153219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 154914
18.0%
r 55921
 
6.5%
o 51147
 
6.0%
h 47595
 
5.5%
e 47539
 
5.5%
l 44369
 
5.2%
i 41925
 
4.9%
n 40146
 
4.7%
d 40103
 
4.7%
u 38090
 
4.4%
Other values (37) 296885
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 722956
84.2%
Uppercase Letter 129335
 
15.1%
Space Separator 3427
 
0.4%
Open Punctuation 1375
 
0.2%
Close Punctuation 1375
 
0.2%
Other Punctuation 166
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 154914
21.4%
r 55921
 
7.7%
o 51147
 
7.1%
h 47595
 
6.6%
e 47539
 
6.6%
l 44369
 
6.1%
i 41925
 
5.8%
n 40146
 
5.6%
d 40103
 
5.5%
u 38090
 
5.3%
Other values (12) 161207
22.3%
Uppercase Letter
ValueCountFrequency (%)
A 14171
11.0%
D 13971
10.8%
C 13839
10.7%
B 13270
10.3%
S 12060
9.3%
K 9418
 
7.3%
R 8552
 
6.6%
H 6657
 
5.1%
V 6554
 
5.1%
T 6358
 
4.9%
Other values (11) 24485
18.9%
Space Separator
ValueCountFrequency (%)
3427
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1375
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1375
100.0%
Other Punctuation
ValueCountFrequency (%)
& 166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 852291
99.3%
Common 6343
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 154914
18.2%
r 55921
 
6.6%
o 51147
 
6.0%
h 47595
 
5.6%
e 47539
 
5.6%
l 44369
 
5.2%
i 41925
 
4.9%
n 40146
 
4.7%
d 40103
 
4.7%
u 38090
 
4.5%
Other values (33) 290542
34.1%
Common
ValueCountFrequency (%)
3427
54.0%
( 1375
21.7%
) 1375
21.7%
& 166
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858634
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 154914
18.0%
r 55921
 
6.5%
o 51147
 
6.0%
h 47595
 
5.5%
e 47539
 
5.5%
l 44369
 
5.2%
i 41925
 
4.9%
n 40146
 
4.7%
d 40103
 
4.7%
u 38090
 
4.4%
Other values (37) 296885
34.6%

type
Categorical

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing1925
Missing (%)1.6%
Memory size1.8 MiB
Residential, Rural and other Areas
44872 
Industrial Area
28814 
Residential and others
25633 
Industrial Areas
16264 
RIRUO
 
1304
Other values (4)
 
899

Length

Max length34
Median length22
Mean length23.777732
Min length5

Characters and Unicode

Total characters2800684
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndustrial Areas
2nd rowIndustrial Areas
3rd rowIndustrial Areas
4th rowIndustrial Areas
5th rowIndustrial Areas

Common Values

ValueCountFrequency (%)
Residential, Rural and other Areas 44872
37.5%
Industrial Area 28814
24.1%
Residential and others 25633
21.4%
Industrial Areas 16264
 
13.6%
RIRUO 1304
 
1.1%
Sensitive Area 371
 
0.3%
Sensitive Areas 323
 
0.3%
Industrial 138
 
0.1%
Residential 67
 
0.1%
(Missing) 1925
 
1.6%

Length

2023-11-18T12:17:25.302303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T12:17:25.499205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
residential 70572
17.9%
and 70505
17.9%
areas 61459
15.6%
industrial 45216
11.5%
rural 44872
11.4%
other 44872
11.4%
area 29185
7.4%
others 25633
 
6.5%
riruo 1304
 
0.3%
sensitive 694
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 321809
11.5%
e 303681
10.8%
276526
9.9%
r 251237
9.0%
s 203574
 
7.3%
i 187748
 
6.7%
n 186987
 
6.7%
t 186987
 
6.7%
d 186293
 
6.7%
l 160660
 
5.7%
Other values (11) 535182
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2220768
79.3%
Space Separator 276526
 
9.9%
Uppercase Letter 258518
 
9.2%
Other Punctuation 44872
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 321809
14.5%
e 303681
13.7%
r 251237
11.3%
s 203574
9.2%
i 187748
8.5%
n 186987
8.4%
t 186987
8.4%
d 186293
8.4%
l 160660
7.2%
u 90088
 
4.1%
Other values (3) 141704
6.4%
Uppercase Letter
ValueCountFrequency (%)
R 118052
45.7%
A 90644
35.1%
I 46520
 
18.0%
U 1304
 
0.5%
O 1304
 
0.5%
S 694
 
0.3%
Space Separator
ValueCountFrequency (%)
276526
100.0%
Other Punctuation
ValueCountFrequency (%)
, 44872
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2479286
88.5%
Common 321398
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 321809
13.0%
e 303681
12.2%
r 251237
10.1%
s 203574
8.2%
i 187748
7.6%
n 186987
7.5%
t 186987
7.5%
d 186293
7.5%
l 160660
6.5%
R 118052
 
4.8%
Other values (9) 372258
15.0%
Common
ValueCountFrequency (%)
276526
86.0%
, 44872
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2800684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 321809
11.5%
e 303681
10.8%
276526
9.9%
r 251237
9.0%
s 203574
 
7.3%
i 187748
 
6.7%
n 186987
 
6.7%
t 186987
 
6.7%
d 186293
 
6.7%
l 160660
 
5.7%
Other values (11) 535182
19.1%

so2
Real number (ℝ)

HIGH CORRELATION 

Distinct2386
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.130261
Minimum0
Maximum909
Zeros788
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:25.740223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15.12
median9
Q313.7
95-th percentile24.6
Maximum909
Range909
Interquartile range (IQR)8.58

Descriptive statistics

Standard deviation12.964224
Coefficient of variation (CV)1.1647727
Kurtosis409.49293
Mean11.130261
Median Absolute Deviation (MAD)4
Skewness13.332617
Sum1332414.7
Variance168.07111
MonotonicityNot monotonic
2023-11-18T12:17:25.921433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9920
 
8.3%
4 5565
 
4.6%
5 3338
 
2.8%
11 3250
 
2.7%
5.275873979 3205
 
2.7%
10 3148
 
2.6%
9 3067
 
2.6%
7 3042
 
2.5%
8 3012
 
2.5%
13 2987
 
2.5%
Other values (2376) 79177
66.1%
ValueCountFrequency (%)
0 788
0.7%
0.2 3
 
< 0.1%
0.3 6
 
< 0.1%
0.4 2
 
< 0.1%
0.47 1
 
< 0.1%
0.5 29
 
< 0.1%
0.6 7
 
< 0.1%
0.7 2
 
< 0.1%
0.8 5
 
< 0.1%
0.9 11
 
< 0.1%
ValueCountFrequency (%)
909 1
< 0.1%
498 1
< 0.1%
492 1
< 0.1%
482 1
< 0.1%
421 1
< 0.1%
407 1
< 0.1%
405 1
< 0.1%
393 1
< 0.1%
380 1
< 0.1%
372 1
< 0.1%

no2
Real number (ℝ)

HIGH CORRELATION 

Distinct3541
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.574397
Minimum0
Maximum592
Zeros769
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:26.140584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8
Q115.6
median22
Q335
95-th percentile76
Maximum592
Range592
Interquartile range (IQR)19.4

Descriptive statistics

Standard deviation24.207636
Coefficient of variation (CV)0.81853354
Kurtosis20.937494
Mean29.574397
Median Absolute Deviation (MAD)8
Skewness3.1229677
Sum3540380.7
Variance586.00965
MonotonicityNot monotonic
2023-11-18T12:17:26.322536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 2989
 
2.5%
21 2704
 
2.3%
22 2400
 
2.0%
19 2329
 
1.9%
18 2125
 
1.8%
23 2106
 
1.8%
17 2104
 
1.8%
10 1962
 
1.6%
13 1959
 
1.6%
24 1909
 
1.6%
Other values (3531) 97124
81.1%
ValueCountFrequency (%)
0 769
0.6%
0.2 1
 
< 0.1%
0.3 1
 
< 0.1%
0.5 4
 
< 0.1%
0.6 2
 
< 0.1%
0.7 4
 
< 0.1%
0.75 1
 
< 0.1%
0.8 2
 
< 0.1%
0.9 7
 
< 0.1%
1 7
 
< 0.1%
ValueCountFrequency (%)
592 1
< 0.1%
484.3 1
< 0.1%
475 1
< 0.1%
449.4 1
< 0.1%
444 1
< 0.1%
439.1 1
< 0.1%
423.5 1
< 0.1%
377 1
< 0.1%
366 1
< 0.1%
353.5 1
< 0.1%

rspm
Real number (ℝ)

HIGH CORRELATION 

Distinct2581
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.16855
Minimum0
Maximum1183.5
Zeros762
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:26.614533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.333333
Q158
median86
Q3119
95-th percentile231
Maximum1183.5
Range1183.5
Interquartile range (IQR)61

Descriptive statistics

Standard deviation68.542161
Coefficient of variation (CV)0.67750461
Kurtosis9.0698813
Mean101.16855
Median Absolute Deviation (MAD)30
Skewness2.2897295
Sum12110989
Variance4698.0279
MonotonicityNot monotonic
2023-11-18T12:17:26.798619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114.7179675 3046
 
2.5%
98.24450975 2207
 
1.8%
196.6397705 1884
 
1.6%
66.58563751 1805
 
1.5%
115.0399092 1168
 
1.0%
86 989
 
0.8%
75 980
 
0.8%
84 972
 
0.8%
82 948
 
0.8%
83.61982436 946
 
0.8%
Other values (2571) 104766
87.5%
ValueCountFrequency (%)
0 762
0.6%
3 19
 
< 0.1%
4 6
 
< 0.1%
5 9
 
< 0.1%
6 8
 
< 0.1%
6.33 1
 
< 0.1%
7 15
 
< 0.1%
7.67 2
 
< 0.1%
8 5
 
< 0.1%
8.33 3
 
< 0.1%
ValueCountFrequency (%)
1183.5 1
< 0.1%
981 1
< 0.1%
892 1
< 0.1%
843 1
< 0.1%
834.5 1
< 0.1%
784 1
< 0.1%
776 1
< 0.1%
735 1
< 0.1%
728 1
< 0.1%
718 2
< 0.1%

spm
Real number (ℝ)

HIGH CORRELATION 

Distinct3971
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.49148
Minimum0
Maximum2366
Zeros900
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:27.029622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q1126.72906
median179
Q3233.50652
95-th percentile400
Maximum2366
Range2366
Interquartile range (IQR)106.77746

Descriptive statistics

Standard deviation117.61571
Coefficient of variation (CV)0.60164109
Kurtosis9.0987696
Mean195.49148
Median Absolute Deviation (MAD)52.270936
Skewness2.0108266
Sum23402480
Variance13833.455
MonotonicityNot monotonic
2023-11-18T12:17:27.215537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7290644 11067
 
9.2%
233.5065239 9457
 
7.9%
167.6098436 8371
 
7.0%
210.0675451 7903
 
6.6%
191.5679298 7372
 
6.2%
399.4020883 3092
 
2.6%
67.25419257 2924
 
2.4%
0 900
 
0.8%
144 300
 
0.3%
175 282
 
0.2%
Other values (3961) 68043
56.8%
ValueCountFrequency (%)
0 900
0.8%
1 6
 
< 0.1%
1.61 2
 
< 0.1%
2 7
 
< 0.1%
2.99 1
 
< 0.1%
3 3
 
< 0.1%
4 7
 
< 0.1%
4.42 1
 
< 0.1%
5 2
 
< 0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
2366 1
< 0.1%
1885 1
< 0.1%
1844 1
< 0.1%
1795 1
< 0.1%
1682 1
< 0.1%
1654 1
< 0.1%
1560 1
< 0.1%
1503 1
< 0.1%
1465 1
< 0.1%
1450 1
< 0.1%

pm2_5
Real number (ℝ)

HIGH CORRELATION 

Distinct442
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.46177
Minimum3
Maximum504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:27.389627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile18.855612
Q130.511628
median42.204089
Q364.890625
95-th percentile95.113208
Maximum504
Range501
Interquartile range (IQR)34.378997

Descriptive statistics

Standard deviation21.743671
Coefficient of variation (CV)0.44867678
Kurtosis5.3684192
Mean48.46177
Median Absolute Deviation (MAD)13.204089
Skewness1.089178
Sum5801407
Variance472.78723
MonotonicityNot monotonic
2023-11-18T12:17:27.577629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.890625 21823
18.2%
29.55044053 20143
16.8%
65.06456454 19091
15.9%
30.72969596 18878
15.8%
42.20408895 16491
13.8%
95.11320755 8180
 
6.8%
18.85561151 4816
 
4.0%
27.88636364 738
 
0.6%
30.51162791 591
 
0.5%
30 322
 
0.3%
Other values (432) 8638
 
7.2%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 4
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 9
 
< 0.1%
8 14
 
< 0.1%
9 52
< 0.1%
9.7 1
 
< 0.1%
9.9 1
 
< 0.1%
10 129
0.1%
ValueCountFrequency (%)
504 1
< 0.1%
395 1
< 0.1%
331 2
< 0.1%
318 1
< 0.1%
304 1
< 0.1%
303 1
< 0.1%
300 1
< 0.1%
297 1
< 0.1%
294 1
< 0.1%
283 1
< 0.1%

date
Date

Distinct4895
Distinct (%)4.1%
Missing1
Missing (%)< 0.1%
Memory size1.8 MiB
Minimum1987-01-01 00:00:00
Maximum2015-12-31 00:00:00
2023-11-18T12:17:27.782639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:27.977742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SOi
Real number (ℝ)

HIGH CORRELATION 

Distinct2386
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.661453
Minimum0
Maximum313.625
Zeros788
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:28.178743image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q16.4
median11.25
Q317.125
95-th percentile30.75
Maximum313.625
Range313.625
Interquartile range (IQR)10.725

Descriptive statistics

Standard deviation12.404329
Coefficient of variation (CV)0.90798017
Kurtosis39.530089
Mean13.661453
Median Absolute Deviation (MAD)5
Skewness4.6605571
Sum1635426.2
Variance153.86737
MonotonicityNot monotonic
2023-11-18T12:17:28.393742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 9920
 
8.3%
5 5565
 
4.6%
6.25 3338
 
2.8%
13.75 3250
 
2.7%
6.594842473 3205
 
2.7%
12.5 3148
 
2.6%
11.25 3067
 
2.6%
8.75 3042
 
2.5%
10 3012
 
2.5%
16.25 2987
 
2.5%
Other values (2376) 79177
66.1%
ValueCountFrequency (%)
0 788
0.7%
0.25 3
 
< 0.1%
0.375 6
 
< 0.1%
0.5 2
 
< 0.1%
0.5875 1
 
< 0.1%
0.625 29
 
< 0.1%
0.75 7
 
< 0.1%
0.875 2
 
< 0.1%
1 5
 
< 0.1%
1.125 11
 
< 0.1%
ValueCountFrequency (%)
313.625 1
< 0.1%
228.0952381 1
< 0.1%
226.6666667 1
< 0.1%
224.2857143 1
< 0.1%
209.7619048 1
< 0.1%
206.4285714 1
< 0.1%
205.952381 1
< 0.1%
203.0952381 1
< 0.1%
200 1
< 0.1%
197.3333333 1
< 0.1%

Noi
Real number (ℝ)

HIGH CORRELATION 

Distinct3541
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.645793
Minimum0
Maximum560
Zeros769
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:28.607802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q119.5
median27.5
Q343.75
95-th percentile95
Maximum560
Range560
Interquartile range (IQR)24.25

Descriptive statistics

Standard deviation28.661477
Coefficient of variation (CV)0.78212191
Kurtosis12.019978
Mean36.645793
Median Absolute Deviation (MAD)10
Skewness2.5391875
Sum4386904.5
Variance821.48029
MonotonicityNot monotonic
2023-11-18T12:17:28.777086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 2989
 
2.5%
26.25 2704
 
2.3%
27.5 2400
 
2.0%
23.75 2329
 
1.9%
22.5 2125
 
1.8%
28.75 2106
 
1.8%
21.25 2104
 
1.8%
12.5 1962
 
1.6%
16.25 1959
 
1.6%
30 1909
 
1.6%
Other values (3531) 97124
81.1%
ValueCountFrequency (%)
0 769
0.6%
0.25 1
 
< 0.1%
0.375 1
 
< 0.1%
0.625 4
 
< 0.1%
0.75 2
 
< 0.1%
0.875 4
 
< 0.1%
0.9375 1
 
< 0.1%
1 2
 
< 0.1%
1.125 7
 
< 0.1%
1.25 7
 
< 0.1%
ValueCountFrequency (%)
560 1
< 0.1%
470.25 1
< 0.1%
462.5 1
< 0.1%
441.1666667 1
< 0.1%
436.6666667 1
< 0.1%
432.5833333 1
< 0.1%
419.5833333 1
< 0.1%
380.8333333 1
< 0.1%
371.6666667 1
< 0.1%
361.25 1
< 0.1%

RSPMi
Real number (ℝ)

ZEROS 

Distinct1267
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.373495
Minimum0
Maximum200
Zeros19323
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:28.944320image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q137
median68
Q398.24451
95-th percentile161.61224
Maximum200
Range200
Interquartile range (IQR)61.24451

Descriptive statistics

Standard deviation48.414249
Coefficient of variation (CV)0.68796141
Kurtosis-0.31849283
Mean70.373495
Median Absolute Deviation (MAD)30.24451
Skewness0.38408194
Sum8424481.4
Variance2343.9395
MonotonicityNot monotonic
2023-11-18T12:17:29.126568image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19323
 
16.1%
128.7158935 3046
 
2.5%
98.24450975 2207
 
1.8%
66.58563751 1805
 
1.5%
129.3663472 1168
 
1.0%
86 989
 
0.8%
75 980
 
0.8%
84 972
 
0.8%
82 948
 
0.8%
83.61982436 946
 
0.8%
Other values (1257) 87327
72.9%
ValueCountFrequency (%)
0 19323
16.1%
3 19
 
< 0.1%
4 6
 
< 0.1%
5 9
 
< 0.1%
6 8
 
< 0.1%
6.33 1
 
< 0.1%
7 15
 
< 0.1%
7.67 2
 
< 0.1%
8 5
 
< 0.1%
8.33 3
 
< 0.1%
ValueCountFrequency (%)
200 215
0.2%
199.3332653 8
 
< 0.1%
199.3265307 2
 
< 0.1%
199.1918367 1
 
< 0.1%
198.9897959 1
 
< 0.1%
198.6530612 3
 
< 0.1%
198.6463265 5
 
< 0.1%
197.9795918 216
0.2%
197.3128571 8
 
< 0.1%
197.3061225 6
 
< 0.1%

SPMi
Real number (ℝ)

HIGH CORRELATION 

Distinct3971
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.98764
Minimum0
Maximum850.23256
Zeros900
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:29.350760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q1117.81938
median152.66667
Q3189.00435
95-th percentile362.5
Maximum850.23256
Range850.23256
Interquartile range (IQR)71.184973

Descriptive statistics

Standard deviation89.826901
Coefficient of variation (CV)0.53792545
Kurtosis2.1109778
Mean166.98764
Median Absolute Deviation (MAD)34.84729
Skewness1.3328627
Sum19990257
Variance8068.8722
MonotonicityNot monotonic
2023-11-18T12:17:29.497767image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.8193762 11067
 
9.2%
189.0043493 9457
 
7.9%
145.0732291 8371
 
7.0%
173.3783634 7903
 
6.6%
161.0452865 7372
 
6.2%
361.7526104 3092
 
2.6%
67.25419257 2924
 
2.4%
0 900
 
0.8%
129.3333333 300
 
0.3%
150 282
 
0.2%
Other values (3961) 68043
56.8%
ValueCountFrequency (%)
0 900
0.8%
1 6
 
< 0.1%
1.61 2
 
< 0.1%
2 7
 
< 0.1%
2.99 1
 
< 0.1%
3 3
 
< 0.1%
4 7
 
< 0.1%
4.42 1
 
< 0.1%
5 2
 
< 0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
850.2325581 1
< 0.1%
738.372093 1
< 0.1%
728.8372093 1
< 0.1%
717.4418605 1
< 0.1%
691.1627907 1
< 0.1%
684.6511628 1
< 0.1%
662.7906977 1
< 0.1%
649.5348837 1
< 0.1%
640.6976744 1
< 0.1%
637.2093023 1
< 0.1%

PMi
Real number (ℝ)

HIGH CORRELATION 

Distinct442
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.416468
Minimum3
Maximum492.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:29.688117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile18.855612
Q130.511628
median42.204089
Q364.890625
95-th percentile95.113208
Maximum492.5
Range489.5
Interquartile range (IQR)34.378997

Descriptive statistics

Standard deviation21.475136
Coefficient of variation (CV)0.44355024
Kurtosis2.7699212
Mean48.416468
Median Absolute Deviation (MAD)13.204089
Skewness0.8678763
Sum5795983.8
Variance461.18146
MonotonicityNot monotonic
2023-11-18T12:17:29.842115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.890625 21823
18.2%
29.55044053 20143
16.8%
65.06456454 19091
15.9%
30.72969596 18878
15.8%
42.20408895 16491
13.8%
95.11320755 8180
 
6.8%
18.85561151 4816
 
4.0%
27.88636364 738
 
0.6%
30.51162791 591
 
0.5%
30 322
 
0.3%
Other values (432) 8638
 
7.2%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 4
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 9
 
< 0.1%
8 14
 
< 0.1%
9 52
< 0.1%
9.7 1
 
< 0.1%
9.9 1
 
< 0.1%
10 129
0.1%
ValueCountFrequency (%)
492.5 1
< 0.1%
345 1
< 0.1%
281 2
< 0.1%
268 1
< 0.1%
254 1
< 0.1%
253 1
< 0.1%
250 1
< 0.1%
247 1
< 0.1%
244 1
< 0.1%
233 1
< 0.1%

AQI
Real number (ℝ)

HIGH CORRELATION 

Distinct3786
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.98134
Minimum0
Maximum850.23256
Zeros768
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-18T12:17:30.030339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q1117.81938
median156.66667
Q3189.00435
95-th percentile362.5
Maximum850.23256
Range850.23256
Interquartile range (IQR)71.184973

Descriptive statistics

Standard deviation87.263164
Coefficient of variation (CV)0.51336907
Kurtosis2.3414812
Mean169.98134
Median Absolute Deviation (MAD)35.333333
Skewness1.46623
Sum20348637
Variance7614.8597
MonotonicityNot monotonic
2023-11-18T12:17:30.223252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.8193762 10025
 
8.4%
189.0043493 9111
 
7.6%
145.0732291 7652
 
6.4%
173.3783634 7486
 
6.3%
161.0452865 7206
 
6.0%
361.7526104 3092
 
2.6%
67.25419257 2110
 
1.8%
0 768
 
0.6%
66.58563751 568
 
0.5%
128.7158935 400
 
0.3%
Other values (3776) 71293
59.6%
ValueCountFrequency (%)
0 768
0.6%
2.5 1
 
< 0.1%
6.25 1
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
17 3
 
< 0.1%
17.33 1
 
< 0.1%
17.5 1
 
< 0.1%
17.67 1
 
< 0.1%
ValueCountFrequency (%)
850.2325581 1
< 0.1%
738.372093 1
< 0.1%
728.8372093 1
< 0.1%
717.4418605 1
< 0.1%
691.1627907 1
< 0.1%
684.6511628 1
< 0.1%
662.7906977 1
< 0.1%
649.5348837 1
< 0.1%
640.6976744 1
< 0.1%
637.2093023 1
< 0.1%

AQI_Range
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Poor
80608 
Moderate
14902 
Unhealthy
9433 
Very unhealthy
 
7254
Hazardous
 
4749

Length

Max length14
Median length4
Mean length5.6962351
Min length4

Characters and Unicode

Total characters681902
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowModerate
3rd rowModerate
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Poor 80608
67.3%
Moderate 14902
 
12.4%
Unhealthy 9433
 
7.9%
Very unhealthy 7254
 
6.1%
Hazardous 4749
 
4.0%
Good 2765
 
2.3%

Length

2023-11-18T12:17:30.398252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T12:17:30.588320image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
poor 80608
63.5%
unhealthy 16687
 
13.1%
moderate 14902
 
11.7%
very 7254
 
5.7%
hazardous 4749
 
3.7%
good 2765
 
2.2%

Most occurring characters

ValueCountFrequency (%)
o 186397
27.3%
r 107513
15.8%
P 80608
11.8%
e 53745
 
7.9%
a 41087
 
6.0%
h 33374
 
4.9%
t 31589
 
4.6%
y 23941
 
3.5%
d 22416
 
3.3%
n 16687
 
2.4%
Other values (10) 84545
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 554937
81.4%
Uppercase Letter 119711
 
17.6%
Space Separator 7254
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 186397
33.6%
r 107513
19.4%
e 53745
 
9.7%
a 41087
 
7.4%
h 33374
 
6.0%
t 31589
 
5.7%
y 23941
 
4.3%
d 22416
 
4.0%
n 16687
 
3.0%
l 16687
 
3.0%
Other values (3) 21501
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
P 80608
67.3%
M 14902
 
12.4%
U 9433
 
7.9%
V 7254
 
6.1%
H 4749
 
4.0%
G 2765
 
2.3%
Space Separator
ValueCountFrequency (%)
7254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 674648
98.9%
Common 7254
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 186397
27.6%
r 107513
15.9%
P 80608
11.9%
e 53745
 
8.0%
a 41087
 
6.1%
h 33374
 
4.9%
t 31589
 
4.7%
y 23941
 
3.5%
d 22416
 
3.3%
n 16687
 
2.5%
Other values (9) 77291
11.5%
Common
ValueCountFrequency (%)
7254
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 681902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 186397
27.3%
r 107513
15.8%
P 80608
11.8%
e 53745
 
7.9%
a 41087
 
6.0%
h 33374
 
4.9%
t 31589
 
4.6%
y 23941
 
3.5%
d 22416
 
3.3%
n 16687
 
2.4%
Other values (10) 84545
12.4%

Interactions

2023-11-18T12:17:20.325461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:00.427623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:02.192330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:04.716501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:06.734768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:08.613245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:10.428610image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:12.620269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:14.427617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:16.702227image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:18.484324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:20.489774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:00.557624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:02.373317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:04.878708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:06.877444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:08.747231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:10.584213image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:12.804360image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:14.586118image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:16.851244image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:18.620255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:20.650857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:00.698645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:02.571505image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:05.051687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:07.011431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:08.886239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:10.787855image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:12.996941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:14.782115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:17.008232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:18.781616image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:20.809859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:00.868143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:02.738508image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:05.219609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:07.231669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:09.052740image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:10.957483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:13.131915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:14.984822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:17.148231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:18.965761image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:20.957317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:01.010229image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:02.874424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:05.385817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:07.353675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:09.202697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:11.122499image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:13.288837image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:15.210900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:17.299817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:19.161941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:21.369698image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:01.162144image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:03.057503image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:05.552736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:07.490673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:09.376796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:11.259480image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:13.449399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:15.386905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:17.456890image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:19.290519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:21.521575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:01.349402image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:03.230498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:05.777160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:07.669749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:09.538796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:11.458486image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:13.602754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:15.613776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:17.609833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:19.451438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:21.685666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:01.508395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:03.381498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:06.011738image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:07.885146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:09.708969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:11.622122image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:13.744778image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:15.823602image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:17.806932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:19.617513image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:21.893070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:01.651404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:03.533507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:06.234685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:08.087582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:09.969893image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:12.044631image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:13.918997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:16.062161image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:17.976254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:19.812515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:22.030008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:01.809313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:04.374600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:06.447661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:08.279958image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:10.151889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:12.224132image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:14.060980image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:16.235999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:18.117251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:19.971433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:22.211454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:01.987312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:04.531168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:06.604750image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:08.454570image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:10.292604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:12.439993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:14.288610image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:16.436037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:18.306252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T12:17:20.143522image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-11-18T12:17:31.092444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
so2no2rspmspmpm2_5SOiNoiRSPMiSPMiPMiAQIstatetypeAQI_Range
so21.0000.2450.2540.140-0.0691.0000.2450.2020.140-0.0690.1540.0360.0280.026
no20.2451.0000.3420.4160.3180.2451.000-0.0670.4160.3180.4230.1910.0460.157
rspm0.2540.3421.0000.6690.4030.2540.3420.2170.6690.4030.7080.1590.0480.298
spm0.1400.4160.6691.0000.5290.1400.4160.0701.0000.5290.9840.1900.0850.559
pm2_5-0.0690.3180.4030.5291.000-0.0690.318-0.0440.5291.0000.5240.3460.0500.155
SOi1.0000.2450.2540.140-0.0691.0000.2450.2020.140-0.0690.1540.0540.0490.045
Noi0.2451.0000.3420.4160.3180.2451.000-0.0670.4160.3180.4230.2540.0620.176
RSPMi0.202-0.0670.2170.070-0.0440.202-0.0671.0000.070-0.0440.1160.2310.0660.332
SPMi0.1400.4160.6691.0000.5290.1400.4160.0701.0000.5290.9840.3370.0880.668
PMi-0.0690.3180.4030.5291.000-0.0690.318-0.0440.5291.0000.5240.3690.0530.155
AQI0.1540.4230.7080.9840.5240.1540.4230.1160.9840.5241.0000.3280.0870.672
state0.0360.1910.1590.1900.3460.0540.2540.2310.3370.3690.3281.0000.1260.343
type0.0280.0460.0480.0850.0500.0490.0620.0660.0880.0530.0870.1261.0000.119
AQI_Range0.0260.1570.2980.5590.1550.0450.1760.3320.6680.1550.6720.3430.1191.000

Missing values

2023-11-18T12:17:22.539739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-18T12:17:23.075646image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-18T12:17:23.636904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

statelocationtypeso2no2rspmspmpm2_5dateSOiNoiRSPMiSPMiPMiAQIAQI_Range
64445Dadra & Nagar HaveliDamanNaN5.26.176.5365375.030.5116286/1/19926.5007.62576.5365375.00000030.51162876.536530Moderate
64446Dadra & Nagar HaveliDamanNaN5.36.076.5365370.030.5116287/1/19926.6257.50076.5365370.00000030.51162876.536530Moderate
64447Dadra & Nagar HaveliDamanNaN6.27.276.5365374.030.5116288/1/19927.7509.00076.5365374.00000030.51162876.536530Moderate
64448Dadra & Nagar HaveliDamanNaN4.55.976.5365375.030.5116289/1/19925.6257.37576.5365375.00000030.51162876.536530Moderate
64449Dadra & Nagar HaveliDamanNaN4.86.476.5365371.030.51162810/1/19926.0008.00076.5365371.00000030.51162876.536530Moderate
64450Dadra & Nagar HaveliDamanNaN5.26.776.5365395.030.51162811/1/19926.5008.37576.5365395.00000030.51162895.000000Moderate
64451Dadra & Nagar HaveliDamanNaN4.66.276.53653125.030.51162812/1/19925.7507.75076.53653116.66666730.511628116.666667Poor
64452Dadra & Nagar HaveliDamanNaN4.96.776.5365399.030.5116281/1/19936.1258.37576.5365399.00000030.51162899.000000Moderate
64453Dadra & Nagar HaveliDamanNaN5.36.876.5365383.030.5116282/1/19936.6258.50076.5365383.00000030.51162883.000000Moderate
64454Dadra & Nagar HaveliDamanNaN5.86.276.5365399.030.5116286/1/19937.2507.75076.5365399.00000030.51162899.000000Moderate
statelocationtypeso2no2rspmspmpm2_5dateSOiNoiRSPMiSPMiPMiAQIAQI_Range
435729West BengalULUBERIARIRUO19.049.0140.0233.50652464.89062511/29/201523.7561.25179.795918189.00434964.890625189.004349Poor
435730West BengalULUBERIARIRUO18.041.0142.0233.50652464.89062512/3/201522.5051.25183.836735189.00434964.890625189.004349Poor
435731West BengalULUBERIARIRUO22.058.0155.0233.50652464.89062512/6/201527.5072.500.000000189.00434964.890625189.004349Poor
435732West BengalULUBERIARIRUO22.050.0145.0233.50652464.89062512/9/201527.5062.50189.897959189.00434964.890625189.897959Poor
435733West BengalULUBERIARIRUO34.061.0161.0233.50652464.89062512/12/201542.5076.250.000000189.00434964.890625189.004349Poor
435734West BengalULUBERIARIRUO20.044.0148.0233.50652464.89062512/15/201525.0055.00195.959184189.00434964.890625195.959184Poor
435735West BengalULUBERIARIRUO17.044.0131.0233.50652464.89062512/18/201521.2555.00161.612245189.00434964.890625189.004349Poor
435736West BengalULUBERIARIRUO18.045.0140.0233.50652464.89062512/21/201522.5056.25179.795918189.00434964.890625189.004349Poor
435737West BengalULUBERIARIRUO22.050.0143.0233.50652464.89062512/24/201527.5062.50185.857143189.00434964.890625189.004349Poor
435738West BengalULUBERIARIRUO20.046.0171.0233.50652464.89062512/29/201525.0057.500.000000189.00434964.890625189.004349Poor

Duplicate rows

Most frequently occurring

statelocationtypeso2no2rspmspmpm2_5dateSOiNoiRSPMiSPMiPMiAQIAQI_Range# duplicates
171Tamil NaduMadrasIndustrial Area11.31513421.60120266.5856380.029.5504412/1/199414.14391827.00150266.5856380.00000029.55044166.585638Moderate3
0Dadra & Nagar HaveliDamanNaN4.9000006.90000076.53653098.030.5116289/1/19946.1250008.62500076.53653098.00000030.51162898.000000Moderate2
1Daman & DiuDaman Diu & NagarIndustrial Area2.8000004.00000073.74943175.027.8863649/1/19943.5000005.00000073.74943175.00000027.88636475.000000Moderate2
2Daman & DiuDaman Diu & NagarIndustrial Area8.60000014.20000073.749431118.027.88636412/1/198910.75000017.75000073.749431112.00000027.886364112.000000Poor2
3Daman & DiuDaman Diu & NagarResidential, Rural and other Areas8.00000010.20000073.749431130.027.88636412/1/198910.00000012.75000073.749431120.00000027.886364120.000000Poor2
4Daman & DiuDaman Diu & NagarResidential, Rural and other Areas9.3000007.10000073.749431134.027.88636412/1/198911.6250008.87500073.749431122.66666727.886364122.666667Poor2
5DelhiDelhiResidential and others9.30000051.600000246.000000506.095.1132085/4/200611.62500064.5000000.000000417.67441995.113208417.674419Hazardous2
6DelhiDelhiResidential, Rural and other Areas12.20000029.200000196.639771477.095.1132089/1/199415.25000036.5000000.000000410.93023395.113208410.930233Hazardous2
7DelhiDelhiResidential, Rural and other Areas12.30000057.700000196.639771328.095.1132089/1/199415.37500072.1250000.000000278.00000095.113208278.000000Unhealthy2
8GoaPanjimNaN0.0000000.0000000.0000000.018.8556126/27/20030.0000000.0000000.0000000.00000018.8556120.000000Good2